AUDIOFILES

1. AUDIOFILES Harika Basana (ilsai@rice.edu ), Elizabeth Chan (lizychan@rice.edu ), Nikolai Sinkov(nik05@rice.edu ), Frank Zhang (frankzsj@rice.edu ) 6100 Main Street, Rice University, Houston, Texas 77005 • GOAL • To explore the MP3 technology and • to implement various audio data compression • algorithms. • Analyze This • Audio compression is to compress an audio file into a smaller-sized file. • People cannot differentiate between these two files by just hearing. • Due to its smaller size, the new file can be easily transferred via the Internet. • People try to find better audio compression algorithms that retain satisfying audio quality. Ding.wav before compression Ding.wav with frequencies within 1 std from the mean Original signal sampled at 44100Hz The x-axis DT sample and the y-axis is the amplitude After linear quantization Ding.wav with frequencies within 2 std from the mean Ding.wav with frequencies within 3 std from the mean After tangent quantization After arctangent quantization • Masking Algorithm • The presence of a signal at a particular frequency can • raise the perceptual threshold of signals close to the • the masking frequency. Procedure • Go through every sample and remove the following samples if they are below a certain threshold. Results • No significant improvement. Need a better way of implementing to get good results. • Algorithms • Average Energy Algorithm • Zeroes out selected high and low frequencies of • the audio file. Procedure • Perform the Discrete Cosine Transform (DCT). • Calculate the signal’s energy. • Find the mean and the standard deviation of from the energy spectrum. • Keep all frequencies with energies within 1 standard deviation (std) from the mean. • Zero out frequencies with energies outside this range. • Similarly, keep frequencies with energies within 2 and 3 stds from the mean. • Perform the Inverse DCT and get the output. Results • Amount of compression is insignificant. • Algorithm would probably work better if the signal is very short, has monotonous tones, and has little noise. • Psycho Acoustic Algorithm • Linear, tangent or arctangent quantization of the • signal. Procedure • Perform the Discrete Cosine Transform (DCT) • Quantize the signal in one of the following ways : Diagram of the quantization “buckets” for the three methods • Give certain frequency bands more bits (1000 – 5100 Hz and 12500 - 15200Hz). • Throw away frequencies below 20Hz and above 20,000Hz. • Perform the Inverse DCT. Results • Compression is very significant. • Quality is good for the amount of compression. • Arctangent quantization yields the best quality. • Conclusion • We didn’t create MP3 files. • Used the underlying concepts. • Produced much smaller files. • Psycho Acoustic Algorithm is the best, in terms of - amount of compression - sound quality of the output. • Improvements • Implement windowing • Implement temporal masking Bibliography: http://www.sospubs.co.uk/sos/may00/articles/mp3.htm http://www.besar.dcs.gla.ac.uk/labs/audiolab/real_site/ tutorials/mp3/mp3how.php and more…